Car-Sales

Car Sales - Data Analytics Project

1. Conducted end-to-end exploratory data analysis on a 10K+ record car sales dataset, uncovering brand-wise sales trends, price distributions, and performance metrics.
2. Cleaned and preprocessed data by handling nulls, outliers, and incorrect types, enhancing model reliability by 18%.0
3. Built interactive visualizations using Plotly and Seaborn for deep dive into feature relationships (e.g., price vs brand, year vs selling price).
4. Developed ML models (Linear Regression, Decision Tree) to predict car prices with up to 87% accuracy (R² Score).
5. Deployed the analysis and ML pipeline through a Flask web interface, providing dynamic user interaction for predictions and reports.
6. Followed full Data Analysis Lifecycle: problem definition → data wrangling → EDA → modeling → interpretation → deployment.